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Background: Disorders affecting cardiac conduction are associated with substantial morbidity. Understanding the epidemiology and risk factors for conduction disorders may enable earlier diagnosis and preventive efforts. Objectives: The purpose of this study was to quantify contemporary frequency and risk factors for electrocardiogram (ECG)-defined cardiac conduction disorders in a large multi-institutional primary care sample. Methods: We quantified prevalence and incidence of conduction disorders among adults receiving longitudinal primary care between 2001 and 2019, each with at least one 12-lead ECG performed prior to the start of follow-up and at least one ECG during follow-up. We defined conduction disorders using curated terms extracted from ECG diagnostic statements by cardiologists. We grouped conduction disorders by inferred anatomic location of abnormal conduction. We tested associations between clinical factors and incident conduction disease using multivariable proportional hazards regression. Results: We analyzed 189,163 individuals (median age 55 years; 58% female). The overall prevalence of conduction disorders was 27% among men and 15% among women. Among 119,926 individuals (median age 55 years; 51% female), 6,802 developed an incident conduction system abnormality over a median of 10 years (Q1, Q3: 6, 15 years) of follow-up. Incident conduction disorders were more common in men (8.78 events/1,000 person-years) vs women (4.34 events/1,000 person-years, P < 0.05). In multivariable models, clinical factors including older age (HR: 1.25 per 5-year increase [95% CI: 1.24-1.26]) and myocardial infarction (HR: 1.39 [95% CI: 1.26-1.54]) were associated with incident conduction disorders. Conclusions: Cardiac conduction disorders are common in a primary care population, especially among older individuals with cardiovascular risk factors.
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BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.
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Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.
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This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.
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Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.
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Enfermedad de la Arteria Coronaria , Registros Electrónicos de Salud , Humanos , Enfermedad de la Arteria Coronaria/genética , Enfermedad de la Arteria Coronaria/epidemiología , Masculino , Femenino , Persona de Mediana Edad , Registros Electrónicos de Salud/estadística & datos numéricos , Anciano , Medición de Riesgo/métodos , Factores de Riesgo , Adulto , Predisposición Genética a la Enfermedad , Inhibidores de Hidroximetilglutaril-CoA Reductasas/uso terapéutico , Reino Unido/epidemiología , Estudios Longitudinales , Herencia Multifactorial/genéticaRESUMEN
Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.
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Fibrilación Atrial , Aprendizaje Profundo , Estudio de Asociación del Genoma Completo , Atrios Cardíacos , Humanos , Fibrilación Atrial/fisiopatología , Fibrilación Atrial/genética , Fibrilación Atrial/diagnóstico por imagen , Atrios Cardíacos/diagnóstico por imagen , Atrios Cardíacos/fisiopatología , Atrios Cardíacos/patología , Masculino , Femenino , Persona de Mediana Edad , Anciano , Imagen por Resonancia Magnética , Análisis de la Aleatorización Mendeliana , Factores de Riesgo , Función del Atrio Izquierdo/fisiología , Volumen Sistólico , Accidente Cerebrovascular , Reino Unido/epidemiología , Sitios Genéticos , Predisposición Genética a la EnfermedadRESUMEN
BACKGROUND: Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS: We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS: Analyses comprised 2384 AVNRT cases and 106â 489 referents, and 2811 AVAP/AVRT cases and 1,483â 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A, SCN10A, and TTN/CCDC141. Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS: Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.
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Estudio de Asociación del Genoma Completo , Taquicardia Supraventricular , Humanos , Taquicardia Supraventricular/genética , Predisposición Genética a la Enfermedad , Taquicardia por Reentrada en el Nodo Atrioventricular/genética , Polimorfismo de Nucleótido Simple , Conectina/genética , TranscriptomaRESUMEN
BACKGROUND: Single-lead electrocardiograms (1L ECGs) are increasingly used for atrial fibrillation (AF) detection. Automated 1L ECG interpretation may have prognostic value for future AF in cases in which screening does not result in a short-term AF diagnosis. OBJECTIVE: We sought to investigate the association between automated 1L ECG interpretation and incident AF. METHODS: VITAL-AF was a randomized controlled trial investigating the effectiveness of screening for AF by 1L ECGs. For this study, participants were divided into 4 groups based on automated classification of 1L ECGs. Patients with prevalent AF were excluded. Associations between groups and incident AF were assessed by Cox proportional hazards models adjusted for risk factors. The start of follow-up was defined as 60 days after the latest 1L ECG (as some individuals had numerous screening 1L ECGs). RESULTS: The study sample included never screened (n = 16,306), normal (n = 10,914), other (n = 2675), and possible AF (n = 561). Possible AF had the highest AF incidence (5.91 per 100 person-years; 95% confidence interval [CI], 4.24-8.23). Possible AF was associated with greater hazard of incident AF compared with normal (adjusted hazard ratio, 2.48; 95% CI, 1.66-3.71). Other was associated with greater hazard of incident AF compared with normal (1.41; 95% CI, 1.04-1.90). CONCLUSION: In patients undergoing AF screening with 1L ECGs without prevalent AF or AF within 60 days of screening, presumptive positive and indeterminate 1L ECG interpretations were associated with future AF. Abnormal 1L ECG recordings may identify individuals at higher risk for future AF.
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AIMS: To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (VËO2peak) without cardiopulmonary exercise testing (CPET). METHODS AND RESULTS: V Ë O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-VËO2'). Associations between estimated VËO2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-VËO2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-VËO2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-VËO2 estimated VËO2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. CONCLUSION: Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (VËO2peak) at scale to enable efficient cardiovascular risk stratification.
Researchers here present data describing a method of estimating exercise capacity from the resting electrocardiogram. Electrocardiogram estimation of exercise capacity was accurate and was found to predict the onset of the wide range of cardiovascular diseases including heart attacks, heart failure, arrhythmia, and death.This approach offers the ability to estimate exercise capacity without dedicated exercise testing and may enable efficient risk stratification of cardiac patients at scale.
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Electrocardiografía , Insuficiencia Cardíaca , Humanos , Femenino , Adulto , Persona de Mediana Edad , Masculino , Pronóstico , Prueba de Esfuerzo/métodos , Consumo de OxígenoRESUMEN
Importance: The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by natural language processing (NLP) may offer a more resource-efficient alternative but this approach has not been validated in a multicenter setting. Objective: To externally validate the Community Care Cohort Project (C3PO) NLP model for heart failure (HF) hospitalization adjudication, which was previously developed and tested within one health care system, compared to gold-standard CEC adjudication in a multicenter clinical trial. Design, Setting, and Participants: This was a retrospective analysis of the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial, which compared 2 influenza vaccines in 5260 participants with cardiovascular disease at 157 sites in the US and Canada between September 2016 and January 2019. Analysis was performed from November 2022 to October 2023. Exposures: Individual sites submitted medical records for each hospitalization. The central INVESTED CEC and the C3PO NLP model independently adjudicated whether the cause of hospitalization was HF using the prepared hospitalization dossier. The C3PO NLP model was fine-tuned (C3PO + INVESTED) and a de novo NLP model was trained using half the INVESTED hospitalizations. Main Outcomes and Measures: Concordance between the C3PO NLP model HF adjudication and the gold-standard INVESTED CEC adjudication was measured by raw agreement, κ, sensitivity, and specificity. The fine-tuned and de novo INVESTED NLP models were evaluated in an internal validation cohort not used for training. Results: Among 4060 hospitalizations in 1973 patients (mean [SD] age, 66.4 [13.2] years; 514 [27.4%] female and 1432 [72.6%] male]), 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was good agreement between the C3PO NLP and CEC HF adjudications (raw agreement, 87% [95% CI, 86-88]; κ, 0.69 [95% CI, 0.66-0.72]). C3PO NLP model sensitivity was 94% (95% CI, 92-95) and specificity was 84% (95% CI, 83-85). The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% (95% CI, 92-94) and κ of 0.82 (95% CI, 0.77-0.86) and 0.83 (95% CI, 0.79-0.87), respectively, vs the CEC. CEC reviewer interrater reproducibility was 94% (95% CI, 93-95; κ, 0.85 [95% CI, 0.80-0.89]). Conclusions and Relevance: The C3PO NLP model developed within 1 health care system identified HF events with good agreement relative to the gold-standard CEC in an external multicenter clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. Further study is needed to determine whether NLP will improve the efficiency of future multicenter clinical trials by identifying clinical events at scale.
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BACKGROUND: Consumer wearable devices with health and wellness features are increasingly common and may enhance disease detection and management. Yet studies informing relationships between wearable device use, attitudes toward device data, and comprehensive clinical profiles are lacking. METHODS AND RESULTS: WATCH-IT (Wearable Activity Tracking for Comprehensive Healthcare-Integrated Technology) studied adults receiving longitudinal primary or ambulatory cardiovascular care in the Mass General Brigham health care system from January 2010 to July 2021. Participants completed a 20-question electronic survey about perceptions and use of consumer wearable devices, with responses linked to electronic health records. Multivariable logistic regression was used to identify factors associated with device use. Among 214 992 individuals receiving longitudinal primary or cardiovascular care with an active electronic portal, 11 121 responded (5.2%). Most respondents (55.8%) currently used a wearable device, and most nonusers (95.3%) would use a wearable if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, most believed it would be very (20.4%) or moderately (34.4%) important to share device-related health information with providers. In multivariable models, older age (odds ratio [OR], 0.80 per 10-year increase [95% CI, 0.77-0.82]), male sex (OR, 0.87 [95% CI, 0.80-0.95]), and heart failure (OR, 0.75 [95% CI, 0.63-0.89]) were associated with lower odds of wearable device use, whereas higher median income (OR, 1.08 per 1-quartile increase [95% CI, 1.04-1.12]) and care in a cardiovascular medicine clinic (OR, 1.17 [95% CI, 1.05-1.30]) were associated with greater odds of device use. CONCLUSIONS: Among patients in primary and cardiovascular medicine clinics, consumer wearable device use is common, and most users perceive value in wearable health data.
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Dispositivos Electrónicos Vestibles , Adulto , Humanos , Masculino , Encuestas y Cuestionarios , Registros Electrónicos de Salud , Actitud , Atención a la SaludRESUMEN
BACKGROUND: Imaging evaluation of arrhythmogenic right ventricular cardiomyopathy (ARVC) remains challenging. Myocardial strain assessment by echocardiography is an increasingly utilized technique for detecting subclinical left ventricular (LV) and right ventricular (RV) dysfunction. We aimed to evaluate the diagnostic and prognostic utility of LV and RV strain in ARVC. METHODS: Patients with suspected ARVC (n = 109) from a multicenter registry were clinically phenotyped using the 2010 ARVC Revised Task Force Criteria and underwent baseline strain echocardiography. Diagnostic performance of LV and RV strain was evaluated using the area under the receiver operating characteristic curve analysis against the 2010 ARVC Revised Task Force Criteria, and the prognostic value was assessed using the Kaplan-Meier analysis. RESULTS: Mean age was 45.3±14.7 years, and 48% of patients were female. Estimation of RV strain was feasible in 99/109 (91%), and LV strain was feasible in 85/109 (78%) patients. ARVC prevalence by 2010 ARVC Revised Task Force Criteria is 91/109 (83%) and 83/99 (84%) in those with RV strain measurements. RV global longitudinal strain and RV free wall strain had diagnostic area under the receiver operating characteristic curve of 0.76 and 0.77, respectively (both P<0.001; difference NS). Abnormal RV global longitudinal strain phenotype (RV global longitudinal strain > -17.9%) and RV free wall strain phenotype (RV free wall strain > -21.2%) were identified in 41/69 (59%) and 56/69 (81%) of subjects, respectively, who were not identified by conventional echocardiographic criteria but still met the overall 2010 ARVC Revised Task Force Criteria for ARVC. LV global longitudinal strain did not add diagnostic value but was prognostic for composite end points of death, heart transplantation, or ventricular arrhythmia (log-rank P=0.04). CONCLUSIONS: In a prospective, multicenter registry of ARVC, RV strain assessment added diagnostic value to current echocardiographic criteria by identifying patients who are missed by current echocardiographic criteria yet still fulfill the diagnosis of ARVC. LV strain, by contrast, did not add incremental diagnostic value but was prognostic for identification of high-risk patients.
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Displasia Ventricular Derecha Arritmogénica , Disfunción Ventricular Derecha , Humanos , Femenino , Adulto , Persona de Mediana Edad , Masculino , Displasia Ventricular Derecha Arritmogénica/diagnóstico por imagen , Displasia Ventricular Derecha Arritmogénica/genética , Estudios Prospectivos , Función Ventricular Derecha , Miocardio , Disfunción Ventricular Derecha/diagnóstico por imagen , Disfunción Ventricular Derecha/etiología , Sistema de RegistrosRESUMEN
BACKGROUND: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function. OBJECTIVES: We developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes. METHODS: We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes. RESULTS: Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures. CONCLUSIONS: Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale.
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Fibrilación Atrial , Aprendizaje Profundo , Insuficiencia Cardíaca , Humanos , Volumen Sistólico , Función Ventricular Izquierda , Estudios RetrospectivosRESUMEN
Currently, coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. We designed a novel and general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. MSGene supports decision making about CAD prevention related to any of these states. We analyzed longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improved discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), with external validation. We also used MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore the potential public health value of our novel multistate model for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics.
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The 12lead electrocardiogram (ECG) is a common and inexpensive diagnostic modality available at scale. The ECG reflects electrical activity throughout the cardiac cycle and is increasingly recognized to contain rich signal relevant across the spectrum of human conditions. Recent work has demonstrated that artificial intelligence (AI)-based algorithms may be able to extract latent information from within the 12lead ECG to classify the presence of disease and even predict the development of future disease. Despite recent development of many AI-based ECG algorithms, comparably few are used in routine clinical practice. Therefore, there is a critical unmet need to identify and mitigate potential barriers to the real-world clinical implementation of AI algorithms. We propose that the adoption of the AI-enabled ECG may be increased by future efforts focused on three key principles: a) maximizing credibility, b) optimizing practicality, and c) establishing clinical utility. In this mini-review, we discuss recent notable work focused on these principles and provide suggestions for future directions. AI-enabled ECG analysis possesses substantial potential to transform current methods to prevent, diagnose, and treat human disease, but a greater emphasis on their real-world application is required to bring that potential to reality.
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Inteligencia Artificial , Electrocardiografía , Humanos , Algoritmos , CorazónRESUMEN
Background: Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm? Methods: Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto. Aortic diameter prediction models were built with the polygenic score ("AORTA Gene") and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us. Results: In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us. AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us. Conclusions: Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.
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Background: The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (NLP) may offer a more resource-efficient alternative. We previously showed that the Community Care Cohort Project (C3PO) NLP model adjudicates heart failure (HF) hospitalizations accurately within one healthcare system. Methods: This study externally validated the C3PO NLP model against CEC adjudication in the INVESTED trial. INVESTED compared influenza vaccination formulations in 5260 patients with cardiovascular disease at 157 North American sites. A central CEC adjudicated the cause of hospitalizations from medical records. We applied the C3PO NLP model to medical records from 4060 INVESTED hospitalizations and evaluated agreement between the NLP and final consensus CEC HF adjudications. We then fine-tuned the C3PO NLP model (C3PO+INVESTED) and trained a de novo model using half the INVESTED hospitalizations, and evaluated these models in the other half. NLP performance was benchmarked to CEC reviewer inter-rater reproducibility. Results: 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was high agreement between the C3PO NLP and CEC HF adjudications (agreement 87%, kappa statistic 0.69). C3PO NLP model sensitivity was 94% and specificity was 84%. The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% and kappa of 0.82 and 0.83, respectively. CEC reviewer inter-rater reproducibility was 94% (kappa 0.85). Conclusion: Our NLP model developed within a single healthcare system accurately identified HF events relative to the gold-standard CEC in an external multi-center clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. NLP may improve the efficiency of future multi-center clinical trials by accurately identifying clinical events at scale.
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Introduction: Consumer wearable devices with health and wellness features are increasingly common and may enhance prevention and management of cardiovascular disease. However, the characteristics and attitudes of wearable device users versus non-users are poorly understood. Methods: Wearable Activity Tracking for Comprehensive Healthcare-Integrated Technology (WATCH-IT) was a prospective study of adults aged ≥18 years receiving longitudinal primary or ambulatory cardiovascular care at one of eleven hospitals within the Mass General Brigham multi-institutional healthcare system between January 2010-July 2021. We invited patients, including wearable users and non-users, to participate via an electronic patient portal. Participants were asked to complete a 20-question survey regarding perceptions and use of consumer wearable devices. Responses were linked to electronic health record data. Multivariable logistic regression was used to identify factors associated with device use. Results: Among 280,834 individuals receiving longitudinal primary or cardiovascular care, 65,842 did not have an active electronic portal or opted out of research contact. Of the 214,992 individuals sent a survey link, 11,121 responded (5.2%), comprising the WATCH-IT patient sample. Most respondents (55.8%) reported current use of a wearable device, and most non-users (95.3%) reported they would use a wearable device if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, the majority believed it would be very (20.4%) or moderately (34.4%) important to share device-related health information with providers. In multivariable models, older age (odds ratio [OR] 0.80 per 10-year increase, 95% CI 0.77-0.82), male sex (0.87, 95% CI 0.80-0.95), and heart failure (0.75, 95% CI 0.63-0.89) were associated with lower odds of wearable device use, whereas higher median zip code income (1.08 per 1-quartile increase, 95% CI 1.04-1.12) and care in a cardiovascular medicine clinic (1.17, 95% CI 1.05-1.30) were associated with greater odds of device use. Nearly all respondents (98%) stated they would share device data with researchers studying health outcomes. Conclusions: Within an electronically assembled cohort of patients in primary and cardiovascular medicine clinics with linkage to detailed health records, wearable device use is common. Most users perceive value in wearable data. Our platform may enable future study of the relationships between wearable technology and resource utilization, clinical outcomes, and health disparities.